The present disclosure relates generally to detecting and monitoring trends in data and, more particularly in some embodiments, to the diagnosis and monitoring of medical conditions from patient deviation data. The present invention relates generally to medical diagnosis and, more particularly, to the diagnosis of medical conditions from patient deviation data.
One type of medical condition or disease that is of interest to the medical community is neurodegenerative disorders (NDDs), such as Alzheimer's disease and Parkinson's disease. Alzheimer's disease currently afflicts tens of millions of people worldwide, and accounts for a majority of dementia cases in patients. Further, there is not, as of yet, any known cure. The economic and social costs associated with Alzheimer's disease are significant, and are increasing over time.
However, NDDs may be challenging to treat and/or study because they are both difficult to detect at an early stage, and hard to quantify in a standardized manner for comparison across different patient populations. In response to these difficulties, investigators have developed methods to determine statistical deviations from normal patient populations. For example, one element of the detection of NDDs is the development of age and tracer segregated normal databases. Comparison to these normals can only happen in a standardized domain, e.g., the Talairach domain or the Montreal Neurological Institute (MNI) domain. The MNI defines a standard brain by using a large series of magnetic resonance imaging (MRI) scans on normal controls. The Talairach domain references a brain that is dissected and photographed for the Talairach and Tournoux atlases. In both the Talairach domain and the MNI domain, data must be mapped to the respective standard domain using registration techniques. Current methods that use a variation of the above method include tracers NeuroQ®, Statistical Parametric matching (SPM), 3D-sterotactic surface projections (3D-SSP), and so forth.
Once a comparison has been made, an image representing a statistical deviation of the anatomy is displayed, allowing a viewer to make a diagnosis based on the image. Making such a diagnosis is a very specialized task and is typically performed by highly-trained medical image experts. However, even such experts can only make a subjective call as to the degree of severity of the disease. Due to this inherent subjectivity, the diagnoses tend to be inconsistent and non-standardized.
Additionally, in numerous medical contexts including but not limited to NDD detection, analysis and reporting of results often takes place in separate informational “silos” that are distinct from one another. For instance, PET & MR exams are read and interpreted by an imaging expert, while blood and cerebro-spinal fluid results are read and interpreted by a laboratory physician. Consequently, in many such instances any diagnosis made by the imaging expert or the laboratory physician may be based on only a portion of relevant patient information available.